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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1901.06261">arXiv:1901.06261</a> <span> [<a href="https://arxiv.org/pdf/1901.06261">pdf</a>, <a href="https://arxiv.org/format/1901.06261">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> NeuNetS: An Automated Synthesis Engine for Neural Network Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sood%2C+A">Atin Sood</a>, <a href="/search/cs?searchtype=author&query=Elder%2C+B">Benjamin Elder</a>, <a href="/search/cs?searchtype=author&query=Herta%2C+B">Benjamin Herta</a>, <a href="/search/cs?searchtype=author&query=Xue%2C+C">Chao Xue</a>, <a href="/search/cs?searchtype=author&query=Bekas%2C+C">Costas Bekas</a>, <a href="/search/cs?searchtype=author&query=Malossi%2C+A+C+I">A. Cristiano I. Malossi</a>, <a href="/search/cs?searchtype=author&query=Saha%2C+D">Debashish Saha</a>, <a href="/search/cs?searchtype=author&query=Scheidegger%2C+F">Florian Scheidegger</a>, <a href="/search/cs?searchtype=author&query=Venkataraman%2C+G">Ganesh Venkataraman</a>, <a href="/search/cs?searchtype=author&query=Thomas%2C+G">Gegi Thomas</a>, <a href="/search/cs?searchtype=author&query=Mariani%2C+G">Giovanni Mariani</a>, <a href="/search/cs?searchtype=author&query=Strobelt%2C+H">Hendrik Strobelt</a>, <a href="/search/cs?searchtype=author&query=Samulowitz%2C+H">Horst Samulowitz</a>, <a href="/search/cs?searchtype=author&query=Wistuba%2C+M">Martin Wistuba</a>, <a href="/search/cs?searchtype=author&query=Manica%2C+M">Matteo Manica</a>, <a href="/search/cs?searchtype=author&query=Choudhury%2C+M">Mihir Choudhury</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+R">Rong Yan</a>, <a href="/search/cs?searchtype=author&query=Istrate%2C+R">Roxana Istrate</a>, <a href="/search/cs?searchtype=author&query=Puri%2C+R">Ruchir Puri</a>, <a href="/search/cs?searchtype=author&query=Pedapati%2C+T">Tejaswini Pedapati</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1901.06261v1-abstract-short" style="display: inline;"> Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through APIs or capability to custom train pre-built neural network architectures with customer data has made the consumption of AI by developers much simpler and resulted in broad adoption of these complex AI models. While prebui… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.06261v1-abstract-full').style.display = 'inline'; document.getElementById('1901.06261v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1901.06261v1-abstract-full" style="display: none;"> Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through APIs or capability to custom train pre-built neural network architectures with customer data has made the consumption of AI by developers much simpler and resulted in broad adoption of these complex AI models. While prebuilt network models exist for certain scenarios, to try and meet the constraints that are unique to each application, AI teams need to think about developing custom neural network architectures that can meet the tradeoff between accuracy and memory footprint to achieve the tight constraints of their unique use-cases. However, only a small proportion of data science teams have the skills and experience needed to create a neural network from scratch, and the demand far exceeds the supply. In this paper, we present NeuNetS : An automated Neural Network Synthesis engine for custom neural network design that is available as part of IBM's AI OpenScale's product. NeuNetS is available for both Text and Image domains and can build neural networks for specific tasks in a fraction of the time it takes today with human effort, and with accuracy similar to that of human-designed AI models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1901.06261v1-abstract-full').style.display = 'none'; document.getElementById('1901.06261v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 12 figures. arXiv admin note: text overlap with arXiv:1806.00250</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.00250">arXiv:1806.00250</a> <span> [<a href="https://arxiv.org/pdf/1806.00250">pdf</a>, <a href="https://arxiv.org/format/1806.00250">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> TAPAS: Train-less Accuracy Predictor for Architecture Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Istrate%2C+R">R. Istrate</a>, <a href="/search/cs?searchtype=author&query=Scheidegger%2C+F">F. Scheidegger</a>, <a href="/search/cs?searchtype=author&query=Mariani%2C+G">G. Mariani</a>, <a href="/search/cs?searchtype=author&query=Nikolopoulos%2C+D">D. Nikolopoulos</a>, <a href="/search/cs?searchtype=author&query=Bekas%2C+C">C. Bekas</a>, <a href="/search/cs?searchtype=author&query=Malossi%2C+A+C+I">A. C. I. Malossi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1806.00250v1-abstract-short" style="display: inline;"> In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.00250v1-abstract-full').style.display = 'inline'; document.getElementById('1806.00250v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.00250v1-abstract-full" style="display: none;"> In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinforcement learning, learning curve extrapolation, and accuracy predictors. None of them, however, demonstrated high-performance without training new experiments in the presence of unseen datasets. We propose a new deep neural network accuracy predictor, that estimates in fractions of a second classification performance for unseen input datasets, without training. In contrast to previously proposed approaches, our prediction is not only calibrated on the topological network information, but also on the characterization of the dataset-difficulty which allows us to re-tune the prediction without any training. Our predictor achieves a performance which exceeds 100 networks per second on a single GPU, thus creating the opportunity to perform large-scale architecture search within a few minutes. We present results of two searches performed in 400 seconds on a single GPU. Our best discovered networks reach 93.67% accuracy for CIFAR-10 and 81.01% for CIFAR-100, verified by training. These networks are performance competitive with other automatically discovered state-of-the-art networks however we only needed a small fraction of the time to solution and computational resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.00250v1-abstract-full').style.display = 'none'; document.getElementById('1806.00250v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.10232">arXiv:1803.10232</a> <span> [<a href="https://arxiv.org/pdf/1803.10232">pdf</a>, <a href="https://arxiv.org/format/1803.10232">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Incremental Training of Deep Convolutional Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Istrate%2C+R">Roxana Istrate</a>, <a href="/search/cs?searchtype=author&query=Malossi%2C+A+C+I">Adelmo Cristiano Innocenza Malossi</a>, <a href="/search/cs?searchtype=author&query=Bekas%2C+C">Costas Bekas</a>, <a href="/search/cs?searchtype=author&query=Nikolopoulos%2C+D">Dimitrios Nikolopoulos</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.10232v1-abstract-short" style="display: inline;"> We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training process. To allow for a smooth dynamic growth of the network, we introduce a look-ahead initialization that outperforms the random initialization. We demonstrate that our incremental approach reaches the reference network… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.10232v1-abstract-full').style.display = 'inline'; document.getElementById('1803.10232v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.10232v1-abstract-full" style="display: none;"> We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training process. To allow for a smooth dynamic growth of the network, we introduce a look-ahead initialization that outperforms the random initialization. We demonstrate that our incremental approach reaches the reference network baseline accuracy. Additionally, it allows to identify smaller partitions of the original state-of-the-art network, that deliver the same final accuracy, by using only a fraction of the global number of parameters. This allows for a potential speedup of the training time of several factors. We report training results on CIFAR-10 for ResNet and VGGNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.10232v1-abstract-full').style.display = 'none'; document.getElementById('1803.10232v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> http://ceur-ws.org/Vol-1998 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.09655">arXiv:1803.09655</a> <span> [<a href="https://arxiv.org/pdf/1803.09655">pdf</a>, <a href="https://arxiv.org/format/1803.09655">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> BAGAN: Data Augmentation with Balancing GAN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mariani%2C+G">Giovanni Mariani</a>, <a href="/search/cs?searchtype=author&query=Scheidegger%2C+F">Florian Scheidegger</a>, <a href="/search/cs?searchtype=author&query=Istrate%2C+R">Roxana Istrate</a>, <a href="/search/cs?searchtype=author&query=Bekas%2C+C">Costas Bekas</a>, <a href="/search/cs?searchtype=author&query=Malossi%2C+C">Cristiano Malossi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.09655v2-abstract-short" style="display: inline;"> Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during the adversarial tr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09655v2-abstract-full').style.display = 'inline'; document.getElementById('1803.09655v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.09655v2-abstract-full" style="display: none;"> Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during the adversarial training all available images of majority and minority classes. The generative model learns useful features from majority classes and uses these to generate images for minority classes. We apply class conditioning in the latent space to drive the generation process towards a target class. The generator in the GAN is initialized with the encoder module of an autoencoder that enables us to learn an accurate class-conditioning in the latent space. We compare the proposed methodology with state-of-the-art GANs and demonstrate that BAGAN generates images of superior quality when trained with an imbalanced dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09655v2-abstract-full').style.display = 'none'; document.getElementById('1803.09655v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.09588">arXiv:1803.09588</a> <span> [<a href="https://arxiv.org/pdf/1803.09588">pdf</a>, <a href="https://arxiv.org/format/1803.09588">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Scheidegger%2C+F">Florian Scheidegger</a>, <a href="/search/cs?searchtype=author&query=Istrate%2C+R">Roxana Istrate</a>, <a href="/search/cs?searchtype=author&query=Mariani%2C+G">Giovanni Mariani</a>, <a href="/search/cs?searchtype=author&query=Benini%2C+L">Luca Benini</a>, <a href="/search/cs?searchtype=author&query=Bekas%2C+C">Costas Bekas</a>, <a href="/search/cs?searchtype=author&query=Malossi%2C+C">Cristiano Malossi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1803.09588v1-abstract-short" style="display: inline;"> In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision towards a class of neural netwo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09588v1-abstract-full').style.display = 'inline'; document.getElementById('1803.09588v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.09588v1-abstract-full" style="display: none;"> In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision towards a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 27x faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search towards promising neural-network configurations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09588v1-abstract-full').style.display = 'none'; document.getElementById('1803.09588v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div 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